Fighting Cancer with Machine learning
Tell Me More

Research Focus

We are building the Cancer Dependency Map!

Precision cancer medicine

We build predictive models to predict cancer vulnerabilities from genomic profiles of tumors and cancer cell lines.

Cancer Targets Identification

We integrate functional screening and 'omics data to identify novel cancer targets as well as drugs for repurposing.

CRISPR/Cas9
screens

We develop computational methods and tools to facilitate the analysis of CRISPR screening in cancer models.

Small-molecule screens

We analyze highly-multiplexed small-molecule screening data from the PRISM platform to discover novel cancer therapeutic leads.

Our Team

Aviad Tsherniak

Associate Director

Philip Montgomery

Sr Principal Software Engineer

Mike Burger

Associate Computational Biologist II

Neekesh Dharia

Postdoctoral Scholar

James McFarland

Data Scientist II

Josephine Lee

Software Engineer

Josh Dempster

Data Scientist

Guillaume Kugener

Associate Computational Biologist I

Zandra Ho

Associate Computational Biologist I

Jordan Rossen

Associate Computational Biologist I

Allie Warren

Associate Computational Biologist I

Andrew Tang

Sr. Visual Designer

Mustafa Kocak

Computational Scientist I

Andy Jones

Associate Computational Biologist I

Phoebe Moh

Associate Software Engineer

Mariya Kazachkova

Associate Computational Biologist I

Vickie Wang

Associate Computational Biologist I

Josh Pan

Postdoctoral fellow

Yejia Chen

Software Engineer

Join Us

Associate Computational Biologist, Target Discovery

Apply here

A major obstacle for treating cancer is a lack of precision medicines. Many potential targeted therapies fail to transition from preclinical models to patients due to incomplete knowledge of the drug’s mechanism of action and/or absence of robust biomarkers to identify relevant patient populations. The Target Discovery arm of the Cancer Dependency Map project aims to provide the oncology community with potential drug targets that have a high likelihood of success.

Our strategy is to develop a more comprehensive understanding of each target’s function in cancer by performing computational analyses that establish associations between the target and the molecular features that predict sensitivity to its perturbation. This information helps guide us in nominating targets that are most likely to translate to patient tumors.

You will be responsible for improving the computational framework for systematically identifying and prioritizing cancer-related gene targets. As part of this effort, you will work with some of the largest experimental cancer biology datasets in the world, including functional genomic (CRISPR, RNAi) and small molecule screens on hundreds of cancer cell lines, and their corresponding multi-omics profiles, e.g. RNA-Seq, whole exome sequencing, and methylation. Experience with software pipeline development is a plus.

Associate Computational Biologist, Flagship

Apply here

As part of the Dependency Map project, several large-scale ‘Flagship’ efforts are now underway. These highly collaborative projects seek to combine insights from large-scale preclinical datasets of cancer vulnerabilities with the expertise of clinician scientists in particular disease areas (e.g. pediatric cancer, GI cancer).

As part of the Flagship DepMap teams, you will work closely with cancer biologists and clinicians to derive new translational insights from large omics and functional screening datasets. These projects will require you to help design experimental and computational research strategies, analyze a broad range of complex, high-dimensional data, and apply a variety of statistical and machine learning tools.

Alumni

Han Xu

Associate Professor, MD Anderson

Robin Meyers

Graduate Student, Genetics, Stanford University

Jared Jacobsen

Studying for AI Research

Li Wang

Computational Biologist, 10X Genomics

Jordan Bryan

Graduate Student, Statistics, Duke University

Kailash Nakagawa

Student, Cambridge Rindge and Latin School

Quinton Wessells

Graduate Student, Biomedical Informatics, Stanford University

Remi Marenco

Bioinformation Lead, Cancer Cell Line Factory

Selected publications

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

Nature Communications

Accompanying website: https://depmap.org/R2-D2/

  • James M. McFarland
  • Zandra V. Ho
  • Guillaume Kugener
  • Joshua M. Dempster
  • Phillip G. Montgomery
  • Jordan G. Bryan
  • John M. Krill-Burger
  • Thomas M. Green
  • Francisca Vazquez
  • Jesse S. Boehm
  • Todd R. Golub
  • William C. Hahn
  • David E. Root
  • Aviad Tsherniak
2018 November

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

Nature Genetics

Accompanying website: https://depmap.org/ceres/

  • Robin M. Meyers
  • Jordan G. Bryan
  • James M. McFarland
  • Barbara A. Weir
  • Ann E. Sizemore
  • Han Xu
  • Neekesh V. Dharia
  • Phillip G. Montgomery
  • Glenn S. Cowley
  • Sasha Pantel
  • Amy Goodale
  • Yenarae Lee
  • Levi D. Ali
  • Guozhi Jiang
  • Rakela Lubonja
  • William F. Harrington
  • Matthew Strickland
  • Ting Wu
  • Derek C. Hawes
  • Victor A. Zhivich
  • Meghan R. Wyatt
  • Zohra Kalani
  • Jaime J. Chang
  • Michael Okamoto
  • Todd R. Golub
  • Jesse S. Boehm
  • Francisca Vazquez
  • David E. Root
  • William C. Hahn
  • Aviad Tsherniak
2017 October

Defining a Cancer Dependency Map

Cell

Accompanying website: https://depmap.org/rnai

  • Aviad Tsherniak
  • Francisca Vazquez
  • Phil G. Montgomery
  • Barbara A. Weir
  • Gregory Kryukov
  • Glenn S. Cowley
  • Stanley Gill
  • William F. Harrington
  • Sasha Pantel
  • John M. Krill-Burger
  • Robin M. Meyers
  • Levi Ali
  • Amy Goodale
  • Yenarae Lee
  • Guozhi Jiang
  • Jessica Hsiao
  • William F.J. Gerath
  • Sara Howell
  • Erin Merkel
  • Mahmoud Ghandi
  • Levi A. Garraway
  • David E. Root
  • Todd R. Golub
  • Jesse S. Boehm
  • William C. Hahn
2017 July

Complementary information derived from CRISPR Cas9 mediated gene deletion and suppression

Nature Communications
  • Rosenbluh J
  • Xu H
  • Harrington W
  • Gill S
  • Wang X
  • Vazquez F
  • Root DE
  • Tsherniak A
  • Hahn WC
2017 May

Genomic copy number dictates a gene-independent cell response to CRISPR-Cas9 targeting

Cancer Discovery
  • Andrew J. Aguirre
  • Robin M. Meyers
  • Barbara A. Weir
  • Francisca Vazquez
  • Cheng-Zhong Zhang
  • Uri Ben-David
  • April Cook
  • Gavin Ha
  • William F. Harrington
  • Mihir B. Doshi
  • Maria Kost-Alimova
  • Stanley Gill
  • Han Xu
  • Levi D. Ali
  • Guozhi Jiang
  • Sasha Pantel
  • Yenarae Lee
  • Amy Goodale
  • Andrew D. Cherniack
  • Coyin Oh
  • Gregory Kryukov
  • Glenn S. Cowley
  • Levi A. Garraway
  • Kimberly Stegmaier
  • Charles W. Roberts
  • Todd R. Golub
  • Matthew Meyerson
  • David E. Root
  • Aviad Tsherniak
  • William C. Hahn
2016 June

MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells

Science
  • Kryukov GV.
  • Wilson FH.
  • Ruth JR.
  • Paulk J.
  • Tsherniak A.
  • Marlow SE.
  • Vazquez F.
  • Weir BA.
  • Fitzgerald ME.
  • Tanaka M.
  • Bielski CM.
  • Scott JM.
  • Dennis C.
  • Cowley GS.
  • Boehm JS.
  • Root DE.
  • Golub TR.
  • Clish CB.
  • Bradner JE.
  • Hahn WC.
  • Garraway LA.
2016 February

Identification of cancer-cytotoxic modulators of PDE3A by predictive chemogenomics

Nature Chemical Biology
  • Luc de Waal
  • Timothy A Lewis
  • Matthew G Rees
  • Aviad Tsherniak
  • Xiaoyun Wu
  • Peter S Choi
  • Lara Gechijian
  • Christina Hartigan
  • Patrick W Faloon
  • Mark J Hickey
  • Nicola Tolliday
  • Steven A Carr
  • Paul A Clemons
  • Benito Munoz
  • Bridget K Wagner
  • Alykhan F Shamji
  • Angela N Koehler
  • Monica Schenone
  • Alex B Burgin
  • Stuart L Schreiber
  • Heidi Greulich
  • Matthew Meyerson
2015 December

ATARiS: Computational quantification of gene suppression phenotypes from multisample RNAi screens

Genome Research
  • Diane D. Shao
  • Aviad Tsherniak
  • Shuba Gopal
  • Barbara A. Weir
  • Pablo Tamayo
  • Nicolas Stransky
  • Steven E. Schumacher
  • Travis I Zack
  • Rameen Beroukhim
  • Levi A. Garraway
  • Adam A. Margolin
  • David E. Root
  • William C. Hahn
  • Jill P. Mesirov
2012 December

Contact Us

Aviad Tsherniak

Cancer Data Science
Broad Institute of MIT and Harvard
415 Main Street
Cambridge, MA 02142

Email: [first name] at broadinstitute.org